coreml by CharlesWiltgen
Use when deploying custom ML models on-device, converting PyTorch models, compressing models, implementing LLM inference, or optimizing CoreML performance. Covers model conversion, compression, stateful models, KV-cache, multi-function models, MLTensor.
DevOps
238 Stars
16 Forks
Updated Jan 16, 2026, 03:16 PM
Why Use This
This skill provides specialized capabilities for CharlesWiltgen's codebase.
Use Cases
- Developing new features in the CharlesWiltgen repository
- Refactoring existing code to follow CharlesWiltgen standards
- Understanding and working with CharlesWiltgen's codebase structure
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License NOASSERTION